A graph-based probabilistic geometric deep learning framework with online enforcement of physical constraints to predict the criticality of defects in porous materials

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

(2023) Saurabh Deshpande et al.   Frontiers in Materials

Addressing materials’ microstructure diversity using transfer learning

(2022) Aurèle Goetz et al.   npj Computational Materials

Graph neural networks for simulating crack coalescence and propagation in brittle materials

(2022) Roberto Perera et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Domain adaptation based transfer learning approach for solving PDEs on complex geometries

(2022) Ayan Chakraborty et al.   ENGINEERING WITH COMPUTERS

Probabilistic deep learning for real-time large deformation simulations

(2022) Saurabh Deshpande et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction

(2021) Haoliang Jiang et al.   JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME

Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning

(2021) Pablo Pereira Álvarez et al.   Mathematics

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Micromechanics-based surrogate models for the response of composites: A critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks

(2020) I.B.C.M. Rocha et al.   EUROPEAN JOURNAL OF MECHANICS A-SOLIDS

Deep learning observables in computational fluid dynamics

(2020) Kjetil O. Lye et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning

(2020) Akhil Thomas et al.   Materials

fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations

(2020) Hanh Vu et al.   NEUROIMAGE

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

(2019) Mateusz Buda et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

(2019) Zhenguo Nie et al.   JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

(2019) Andrew W. Senior et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Simulation of hyperelastic materials in real-time using deep learning

(2019) Andrea Mendizabal et al.   MEDICAL IMAGE ANALYSIS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A fast, certified and “tuning free” two-field reduced basis method for the metamodelling of affinely-parametrised elasticity problems

(2016) K.C. Hoang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Reduced Basis Multiscale Finite Element Methods for Elliptic Problems

(2015) Jan S. Hesthaven et al.   MULTISCALE MODELING & SIMULATION

A three-scale domain decomposition method for the 3D analysis of debonding in laminates

(2009) Pierre Kerfriden et al.   COMPUTATIONAL MECHANICS

Non-intrusive and exact global/local techniques for structural problems with local plasticity

(2009) Lionel Gendre et al.   COMPUTATIONAL MECHANICS

Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities

(2009) Christophe Geuzaine et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Hyper-reduction of mechanical models involving internal variables

(2008) D. Ryckelynck   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING